The present invention relates generally to the field of medical imaging. Specifically, the invention relates to a technique for automatically extracting structures, particularly three-dimensional structures, from a volume dataset.
A volume dataset may be generated by a variety of medical imaging technologies or modalities. For example, magnetic resonance imaging (MRI) modalities generate volume datasets by exposing a patient to magnetic field and by measuring the response and realignment of various magnetically susceptible molecule types within the body. By imposing weak gradient fields along the three-dimensional axes during the process, positional information may be obtained which allows a volume dataset to be constructed. Other technologies, such computed tomography (CT), measure the attenuation of streams of radiation through the body from numerous angles. The attenuation information may be combined and reconstructed to generate a volume dataset. Still other imaging modalities, such as various nuclear imaging technologies, measure the detectable emissions generated by labeled molecules, such as radionuclides or radiopharmaceuticals, to provide volume data. Though the manner in which a volume dataset is created may vary, with the above techniques representing only a sample, the analysis of volume datasets raise many common issues.
For instance, it is often desirable to segment and extract image data corresponding to contiguous and/or complex structures from the background volume for analysis. For example, in the field of CT angiography (CTA), the vascular and other circulatory system structures may be imaged, typically by administration of a radio-opaque dye prior to imaging. Visualization of the CTA data typically is performed in a two-dimensional manner, i.e., slice-by-slice, or in a three-dimensional manner, i.e., volume visualization, which allows the data to be analyzed for vascular pathologies. For example, the data may be analyzed for aneurysms, vascular calcification, renal donor assessment, stent placement, vascular blockage, and vascular evaluation for sizing and/or runoff. Once a pathology is located, quantitative assessments of the pathology may be made of the on the original two-dimensional slices.
As one might expect, segmentation and extraction of complex structures, such as the vasculature in the preceding CTA example, may benefit from accurate segmentation, i.e., identification, of the image data corresponding to the structure of interest. Similarly, quantitative assessment of located pathologies, as noted above, may also benefit from accurate segmentation. Existing segmentation techniques, however, may incorrectly incorporate background or proximate objects into the segmented structure due to poor recognition of edges or non-homogeneities in the image data. Similarly, existing techniques may improperly exclude image data from the segmented structure due to poor edge recognition and/or non-homogeneities. Such exclusions may potentially result in early or erroneous termination of the segmentation technique. Furthermore, splits or mergers in the structure of interest may not be properly segmented by existing techniques due to these shortcomings. In addition, anatomic and pathological variability within the patient population, such as due to plaque deposits in the vasculature or to the presence of interventional devices, such as stents, may further confound the segmentation process.
For example, in CTA, overlapping image intensities, close proximity of imaged structures, and limited detector resolution may make the automated separation of bone and vascular structures difficult. In particular, the proximity of vascular structures and bone in the head and neck region, along the vertebra and near the pelvis make segmentation an exceedingly complex task for computer-based algorithms. The presence of calcification or interventional devices may compound these difficulties.
As a result, proper segmentation of a complex or contiguous three-dimensional structure, such as the vasculature around the head and neck region, may require operator intervention or input. In particular, operator intervention may be needed to designate initial start points and/or to prevent the inadvertent inclusion or exclusions of volume data from the segmented structure. This operator intervention can lead to undesirable delays as well as to inter- and intra-user variability in the segmentation of structures. There is a need therefore, for an improved technique for segmenting structure in a volume, preferably with little or no human intervention.